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Stone Group Corporation has carved out a historic role in opening up new avenues in China's high-tech industries. It is the company that first brought to the market an integrated Chinese word processor, a product that revolutionized Chinese word-processing, and it has dominated the market ever since. Stone is well known for being the largest non-governmental enterprise in China. The company's market success in new high-tech products is considered to be closely related to the organizational framework that evolved at Stone. This chapter documents the characteristics of Stone's new organizational structure and the strengths and limitations of that structure for allocating the resources and creating the incentives required for organizational learning.

The chapter discusses the computerization of the English language, a phenomenon called infomania. The advent of word processor technology made modern verbal life faster and caused a cultural change wherein the bulk of written English is now on a computer. The chapter also discusses the evolution of technology from the crude microcomputers before 1980 into the more complex electronic spreadsheets and today's word processors. While the development of the computer may have positively affected the pace with which data is processed and spread, it has also brought about some pathological issues such as increased stress, mindless productivity, and even physical hazard to a person's eyes. There is also an introduction to the philosophical underpinnings of the infomania phenomenon, especially those observed by Martin Heidegger. In the end, the chapter warns of the diminishing significance of information at a time when it is so easily accessed.

An analysis of technologies familiar to Husserl in his daily life with a postphenomenological emphasis upon embodiment and the differences between different technologies. Includes eyeglasses, magnifying glasses, pens, typewriters and history to word processing.

This chapter focuses on stationery shops that sell writing materials and suggests that, after the decline of writing, they will also disappear from our environment in the face of the informatic revolution. One might argue that stationery shops should not be singled out from all the others, that all shops are condemned to disappear with the decline of writing. But stationery shops are different. They will become superfluous as shops, but so will the things they have for sale, particularly the writing paper. The informatic revolution is not only a political one—we are losing the city—but also a cultural revolution that is causing the culture of writing to disappear. The loss of a culture of writing can be observed in office stationeries. In addition to typewriters and even older writing tools, they carry more and more word processors, which are evidently geared toward replacement by higher-function artificial intelligences that no longer use any paper. The design of stationery stores buries, rather than praise, paper.

There is no such person as the inventor of the computer: it was a group effort. The many pioneers involved worked in different places and at different times, some in relative isolation and others within collaborative research networks. There are some very famous names among them, such as Charles Babbage and John von Neumann—and, of course, Alan Turing himself. Other leading names in this roll of honour include Konrad Zuse, Tommy Flowers, Howard Aiken, John Atanasoff, John Mauchly, Presper Eckert, Jay Forrester, Harry Huskey, Julian Bigelow, Samuel Alexander, Ralph Slutz, Trevor Pearcey, Maurice Wilkes, Max Newman, Freddie Williams, and Tom Kilburn. Turing’s own outstanding contribution was to invent what he called the ‘universal computing machine’. He was first to describe the basic logical principles of the modern computer, writing these down in 1936, 12 years before the appearance of the earliest implementation of his ideas. This came in 1948, when Williams and Kilburn succeeded in wiring together the first electronic universal computing machine—the first modern electronic computer. In 1936, at the age of just 23, Turing invented the fundamental logical principles of the modern computer—almost by accident. A shy boyish-looking genius, he had recently been elected a Fellow of King’s College, Cambridge. The young Turing worked alone, in a spartan room at the top of an ancient stone building beside the River Cam. It was all quite the opposite of a modern research facility—Cambridge’s scholars had been doing their thinking in comfortless stone buildings, reminiscent of cathedrals or monasteries, ever since the university had begun to thrive in the Middle Ages. A few steps from King’s, along narrow medieval lanes, are the buildings and courtyards where, in the seventeenth century, Isaac Newton revolutionized our understanding of the universe. Turing was about to usher in another revolution. He was engaged in theoretical work in the foundations of mathematics. No-one could have guessed that anything of practical value would emerge from his highly abstract research, let alone a machine that would change all our lives.

The modern computer age began on 21 June 1948, when the first electronic universal stored-program computer successfully ran its first program. Built in Manchester, this ancestral computer was the world’s first universal Turing machine in hardware. Fittingly, it was called simply ‘Baby’. The story of Turing’s involvement with Baby and with its successors at Manchester is a tangled one. The world’s first electronic stored-program digital computer ran its first program in the summer of 1948 (Fig. 20.1). ‘A small electronic digital computing machine has been operating successfully for some weeks in the Royal Society Computing Machine Laboratory’, wrote Baby’s designers, Freddie Williams and Tom Kilburn, in the letter to the scientific periodical Nature that announced their success to the world. Williams, a native of the Manchester area, had spent his war years working on radar in rural Worcestershire. Kilburn, his assistant, was a bluntspeaking Yorkshireman. By the end of the fighting there wasn’t much that, between them, they didn’t know about the state of the art in electronics. In December 1945 the two friends returned to the north of England to pioneer the modern computer. Baby was a classic case of a small-scale university pilot project that led to successful commercial development by an external company. The Manchester engineering firm Ferranti built its Ferranti Mark I computer to Williams’s and Kilburn’s design: this was the earliest commercially available electronic digital computer. The first Ferranti rolled out of the factory in February 1951. UNIVAC I, the earliest computer to go on the market in the United States, came a close second: the first one was delivered a few weeks later, in March 1951. Williams and Kilburn developed a high-speed memory for Baby that went on to become a mainstay of computing worldwide. It consisted of cathode-ray tubes resembling small television tubes. Data (zeros and ones) were stored as a scatter of dots on each tube’s screen: a small focused dot represented ‘1’ and a larger blurry dot represented ‘0’. The Williams tube memory, as the invention was soon called, was also used in Baby’s immediate successors, built at Manchester University and by Ferranti Ltd.

We come at last to the forbidden first person, the I am. No story is right until the teller is part of it. Yet a peculiar mischief is abroad in the land of science and engineering. It is a mischief born out of the noblest of intentions. For decades it has spread like the flu, far beyond the technical journals that gave it birth. The intention is to let us stand like blindfolded Justice—pure, objective, and aloof. To do this, we write about our work without ever speaking in the first person. We try to let fact speak for itself. Instead of saying, “I solved the equation and got y = log x”, we write, “The solution of the equation is y = log x”. We turn our actions into facts that are untouched by human hands. To some extent we must do that. Our facts should be sufficiently solid that we do not need to prop them up with our desires. Third-person detachment has its place, but my own person is not so easy to erase. Suppose I think another engineer, whom I shall call Hoople, is wrong. I am not objective about Hoople, but I must appear to be. So I write, “It is believed that Hoople is incorrect.” That’s a cheap shot. I express my thoughts without taking responsibility for them. I seem to be reporting general disapproval of Hoople. In the unholy name of objectivity, I make it sound as though the whole profession thinks that Hoople is a fool. Now radio and TV journalists are doing it. I cringe every time I hear, “It is expected that Congress will pass the bill. “Who expects that? The announcer? The Democrats? A government official? Maybe the soy sauce lobby is the expectant source. So instead of objectivity we get obfuscation. If our work really occurred in objective isolation, we could write about it that way. But people are present. They think and they act. If we fail to represent human intervention accurately, we are dishonest, and objectivity becomes meaningless. The things we make tell the world what we are.

This chapter is a tutorial on using R. To get the most out of it, you should open an R session and type the commands into the console as you read the text. You should be able to use copy-and-paste if you have access to an electronic version of the book. All code is available on the book’s Web site. Science requires transparency and reproducibility. The R language for statistical modeling makes this easy. Developing, maintaining, and documenting your R code is simple. R contains numerous functions for organizing, graphing, and modeling your data. Directions for obtaining R, accompanying packages, and other sources of documentation are available at http://www.r-project.org/. Anyone serious about applying statistics to climate data should learn R. The book is self-contained. It presents R code and data (or links to data) that can be copied to reproduce the graphs and tables. This reproducibility provides you with an enhanced learning opportunity. Here we present a tutorial to help you get started. This can be skipped if you already know how to work with R. R is the ‘lingua franca’ of data analysis and statistical computing. It helps you perform a variety of computing tasks by giving you access to commands. This is similar to other programming languages such as Python and C++. R is particularly useful to researchers because it contains a number of built-in functions for organizing data, performing calculations, and creating graphics. R is an open-source statistical environment modeled after S. The S language was developed in the late 1980s at AT&T labs. The R project was started by Robert Gentleman and Ross Ihaka of the Statistics Department of the University of Auckland in 1995. It now has a large audience. It is currently maintained by the R core-development team, an international group of volunteer developers. To get to the R project Web site, open a browser and, in the search window, type the keywords “R project” or directly link to the Web page using http://www.r-project.org/. Directions for obtaining the software, accompanying packages, and other sources of documentation are provided at the site.